Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations141250
Missing cells705426
Missing cells (%)15.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory203.3 MiB
Average record size in memory1.5 KiB

Variable types

Text6
Categorical14
Numeric6
Unsupported1
Boolean1
DateTime5

Alerts

Card Expiration Date has constant value "12/28" Constant
Bank Name has constant value "Bank of Example" Constant
Bank Account Type has constant value "Checking" Constant
AVS Result Code has constant value "True" Constant
CVV/CVC Result Code has constant value "M" Constant
Fraud Action has constant value "allow" Constant
Card Brand is highly overall correlated with Merchant Processor Type and 2 other fieldsHigh correlation
Card Last 4 Digits is highly overall correlated with Payment Gateway Type and 1 other fieldsHigh correlation
Gateway Net Settled Amount is highly overall correlated with Net Amount to Merchant and 3 other fieldsHigh correlation
Merchant Account ID is highly overall correlated with Merchant Processor Type and 3 other fieldsHigh correlation
Merchant Processor Type is highly overall correlated with Card Brand and 4 other fieldsHigh correlation
Net Amount to Merchant is highly overall correlated with Gateway Net Settled Amount and 3 other fieldsHigh correlation
Payment Gateway Type is highly overall correlated with Card Brand and 5 other fieldsHigh correlation
Payment Method Type is highly overall correlated with Card Brand and 5 other fieldsHigh correlation
Processor Fees is highly overall correlated with Merchant Processor Type and 2 other fieldsHigh correlation
Processor Response Message is highly overall correlated with Gateway Net Settled Amount and 4 other fieldsHigh correlation
Transaction Amount is highly overall correlated with Gateway Net Settled Amount and 3 other fieldsHigh correlation
Transaction Currency is highly overall correlated with Merchant Account IDHigh correlation
Transaction Status is highly overall correlated with Processor Response Message and 1 other fieldsHigh correlation
Transaction Type is highly overall correlated with Gateway Net Settled Amount and 4 other fieldsHigh correlation
Transaction Type is highly imbalanced (68.6%) Imbalance
Transaction Status is highly imbalanced (82.3%) Imbalance
Processor Response Message is highly imbalanced (68.6%) Imbalance
Card Brand has 93954 (66.5%) missing values Missing
Card Last 4 Digits has 93954 (66.5%) missing values Missing
Bank Name has 94406 (66.8%) missing values Missing
Bank Account Type has 94406 (66.8%) missing values Missing
Payment Method Token has 46844 (33.2%) missing values Missing
AVS Result Code has 93954 (66.5%) missing values Missing
CVV/CVC Result Code has 93954 (66.5%) missing values Missing
Acquirer Reference Number (ARN) has 93954 (66.5%) missing values Missing
Merchant Order ID has unique values Unique
Processor Response Code is an unsupported type, check if it needs cleaning or further analysis Unsupported
Processor Fees has 93954 (66.5%) zeros Zeros

Reproduction

Analysis started2025-07-06 23:53:35.536307
Analysis finished2025-07-06 23:53:43.121504
Duration7.59 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct141176
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
2025-07-06T16:53:43.263658image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters2401250
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141102 ?
Unique (%)99.9%

Sample

1st rowPROC-TRX-96148747
2nd rowPROC-TRX-53756634
3rd rowPROC-TRX-96643401
4th rowPROC-TRX-48503361
5th rowPROC-TRX-87613870
ValueCountFrequency (%)
proc-trx-62682136 2
 
< 0.1%
proc-trx-20751798 2
 
< 0.1%
proc-trx-79438389 2
 
< 0.1%
proc-trx-25755157 2
 
< 0.1%
proc-trx-59325275 2
 
< 0.1%
proc-trx-11723443 2
 
< 0.1%
proc-trx-64419314 2
 
< 0.1%
proc-trx-46097790 2
 
< 0.1%
proc-trx-34038773 2
 
< 0.1%
proc-trx-31233803 2
 
< 0.1%
Other values (141166) 141230
> 99.9%
2025-07-06T16:53:43.422331image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 282500
 
11.8%
R 282500
 
11.8%
P 141250
 
5.9%
O 141250
 
5.9%
C 141250
 
5.9%
T 141250
 
5.9%
X 141250
 
5.9%
4 115131
 
4.8%
8 114808
 
4.8%
2 114778
 
4.8%
Other values (7) 785283
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2401250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 282500
 
11.8%
R 282500
 
11.8%
P 141250
 
5.9%
O 141250
 
5.9%
C 141250
 
5.9%
T 141250
 
5.9%
X 141250
 
5.9%
4 115131
 
4.8%
8 114808
 
4.8%
2 114778
 
4.8%
Other values (7) 785283
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2401250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 282500
 
11.8%
R 282500
 
11.8%
P 141250
 
5.9%
O 141250
 
5.9%
C 141250
 
5.9%
T 141250
 
5.9%
X 141250
 
5.9%
4 115131
 
4.8%
8 114808
 
4.8%
2 114778
 
4.8%
Other values (7) 785283
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2401250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 282500
 
11.8%
R 282500
 
11.8%
P 141250
 
5.9%
O 141250
 
5.9%
C 141250
 
5.9%
T 141250
 
5.9%
X 141250
 
5.9%
4 115131
 
4.8%
8 114808
 
4.8%
2 114778
 
4.8%
Other values (7) 785283
32.7%

Merchant Account ID
Categorical

High correlation 

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.2 MiB
MERCH_CYBERSOURCE_USD
28475 
MERCH_GOCARDLESS_USD
28156 
MERCH_PAYPAL_USD
28153 
MERCH_CYBERSOURCE_EUR
7097 
MERCH_PAYPAL_EUR
6988 
Other values (10)
42381 

Length

Max length21
Median length20
Mean length19.00075
Min length16

Characters and Unicode

Total characters2683856
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMERCH_PAYPAL_USD
2nd rowMERCH_GOCARDLESS_USD
3rd rowMERCH_GOCARDLESS_USD
4th rowMERCH_CYBERSOURCE_GBP
5th rowMERCH_PAYPAL_USD

Common Values

ValueCountFrequency (%)
MERCH_CYBERSOURCE_USD 28475
20.2%
MERCH_GOCARDLESS_USD 28156
19.9%
MERCH_PAYPAL_USD 28153
19.9%
MERCH_CYBERSOURCE_EUR 7097
 
5.0%
MERCH_PAYPAL_EUR 6988
 
4.9%
MERCH_GOCARDLESS_EUR 6917
 
4.9%
MERCH_PAYPAL_GBP 4764
 
3.4%
MERCH_GOCARDLESS_GBP 4711
 
3.3%
MERCH_CYBERSOURCE_GBP 4625
 
3.3%
MERCH_PAYPAL_AUD 3841
 
2.7%
Other values (5) 17523
12.4%

Length

2025-07-06T16:53:43.455916image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
merch_cybersource_usd 28475
20.2%
merch_gocardless_usd 28156
19.9%
merch_paypal_usd 28153
19.9%
merch_cybersource_eur 7097
 
5.0%
merch_paypal_eur 6988
 
4.9%
merch_gocardless_eur 6917
 
4.9%
merch_paypal_gbp 4764
 
3.4%
merch_gocardless_gbp 4711
 
3.3%
merch_cybersource_gbp 4625
 
3.3%
merch_paypal_aud 3841
 
2.7%
Other values (5) 17523
12.4%

Most occurring characters

ValueCountFrequency (%)
E 303688
11.3%
R 303688
11.3%
C 292634
10.9%
_ 282500
10.5%
S 225768
8.4%
U 164498
 
6.1%
A 162428
 
6.1%
D 152992
 
5.7%
M 141250
 
5.3%
H 141250
 
5.3%
Other values (6) 513160
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2683856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 303688
11.3%
R 303688
11.3%
C 292634
10.9%
_ 282500
10.5%
S 225768
8.4%
U 164498
 
6.1%
A 162428
 
6.1%
D 152992
 
5.7%
M 141250
 
5.3%
H 141250
 
5.3%
Other values (6) 513160
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2683856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 303688
11.3%
R 303688
11.3%
C 292634
10.9%
_ 282500
10.5%
S 225768
8.4%
U 164498
 
6.1%
A 162428
 
6.1%
D 152992
 
5.7%
M 141250
 
5.3%
H 141250
 
5.3%
Other values (6) 513160
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2683856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 303688
11.3%
R 303688
11.3%
C 292634
10.9%
_ 282500
10.5%
S 225768
8.4%
U 164498
 
6.1%
A 162428
 
6.1%
D 152992
 
5.7%
M 141250
 
5.3%
H 141250
 
5.3%
Other values (6) 513160
19.1%

Merchant Processor Type
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
PayPal
47110 
GoCardless
46844 
ClientLine
31657 
Amex
15639 

Length

Max length10
Median length10
Mean length8.0016
Min length4

Characters and Unicode

Total characters1130226
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowGoCardless
3rd rowGoCardless
4th rowClientLine
5th rowPayPal

Common Values

ValueCountFrequency (%)
PayPal 47110
33.4%
GoCardless 46844
33.2%
ClientLine 31657
22.4%
Amex 15639
 
11.1%

Length

2025-07-06T16:53:43.485926image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:43.510727image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
paypal 47110
33.4%
gocardless 46844
33.2%
clientline 31657
22.4%
amex 15639
 
11.1%

Most occurring characters

ValueCountFrequency (%)
a 141064
12.5%
e 125797
11.1%
l 125611
11.1%
P 94220
 
8.3%
s 93688
 
8.3%
C 78501
 
6.9%
i 63314
 
5.6%
n 63314
 
5.6%
y 47110
 
4.2%
G 46844
 
4.1%
Other values (8) 250763
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1130226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 141064
12.5%
e 125797
11.1%
l 125611
11.1%
P 94220
 
8.3%
s 93688
 
8.3%
C 78501
 
6.9%
i 63314
 
5.6%
n 63314
 
5.6%
y 47110
 
4.2%
G 46844
 
4.1%
Other values (8) 250763
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1130226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 141064
12.5%
e 125797
11.1%
l 125611
11.1%
P 94220
 
8.3%
s 93688
 
8.3%
C 78501
 
6.9%
i 63314
 
5.6%
n 63314
 
5.6%
y 47110
 
4.2%
G 46844
 
4.1%
Other values (8) 250763
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1130226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 141064
12.5%
e 125797
11.1%
l 125611
11.1%
P 94220
 
8.3%
s 93688
 
8.3%
C 78501
 
6.9%
i 63314
 
5.6%
n 63314
 
5.6%
y 47110
 
4.2%
G 46844
 
4.1%
Other values (8) 250763
22.2%

Transaction Type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
payment
125000 
refund
 
12500
chargeback
 
2500
chargeback_reversal
 
1250

Length

Max length19
Median length7
Mean length7.0707965
Min length6

Characters and Unicode

Total characters998750
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpayment
2nd rowpayment
3rd rowpayment
4th rowpayment
5th rowpayment

Common Values

ValueCountFrequency (%)
payment 125000
88.5%
refund 12500
 
8.8%
chargeback 2500
 
1.8%
chargeback_reversal 1250
 
0.9%

Length

2025-07-06T16:53:43.544038image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:43.567485image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
payment 125000
88.5%
refund 12500
 
8.8%
chargeback 2500
 
1.8%
chargeback_reversal 1250
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 998750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 143750
14.4%
n 137500
13.8%
a 133750
13.4%
p 125000
12.5%
y 125000
12.5%
m 125000
12.5%
t 125000
12.5%
r 18750
 
1.9%
u 12500
 
1.3%
d 12500
 
1.3%
Other values (10) 40000
 
4.0%

Transaction Status
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
settled
137500 
disputed
 
3750

Length

Max length8
Median length7
Mean length7.0265487
Min length7

Characters and Unicode

Total characters992500
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsettled
2nd rowsettled
3rd rowsettled
4th rowsettled
5th rowsettled

Common Values

ValueCountFrequency (%)
settled 137500
97.3%
disputed 3750
 
2.7%

Length

2025-07-06T16:53:43.598419image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:43.618965image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
settled 137500
97.3%
disputed 3750
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 992500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 992500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 992500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 278750
28.1%
t 278750
28.1%
d 145000
14.6%
s 141250
14.2%
l 137500
13.9%
i 3750
 
0.4%
p 3750
 
0.4%
u 3750
 
0.4%

Transaction Amount
Real number (ℝ)

High correlation 

Distinct122801
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1979.2208
Minimum-4999.85
Maximum4999.87
Zeros0
Zeros (%)0.0%
Negative15000
Negative (%)10.6%
Memory size1.1 MiB
2025-07-06T16:53:43.650784image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-4999.85
5-th percentile-2655.288
Q1828.465
median2221.17
Q33607.4425
95-th percentile4724.491
Maximum4999.87
Range9999.72
Interquartile range (IQR)2778.9775

Descriptive statistics

Standard deviation2112.685
Coefficient of variation (CV)1.0674327
Kurtosis1.0466637
Mean1979.2208
Median Absolute Deviation (MAD)1389.74
Skewness-1.0062879
Sum2.7956493 × 108
Variance4463437.9
MonotonicityNot monotonic
2025-07-06T16:53:43.689651image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4756.12 5
 
< 0.1%
953.97 5
 
< 0.1%
931.3 5
 
< 0.1%
3543.24 5
 
< 0.1%
4146.9 5
 
< 0.1%
1020.37 5
 
< 0.1%
1902 5
 
< 0.1%
2595.9 5
 
< 0.1%
1121 4
 
< 0.1%
4540.24 4
 
< 0.1%
Other values (122791) 141202
> 99.9%
ValueCountFrequency (%)
-4999.85 1
< 0.1%
-4999.06 1
< 0.1%
-4998.84 2
< 0.1%
-4998.76 1
< 0.1%
-4998.23 1
< 0.1%
-4996.55 2
< 0.1%
-4996.24 1
< 0.1%
-4996.23 2
< 0.1%
-4996.21 2
< 0.1%
-4995.93 1
< 0.1%
ValueCountFrequency (%)
4999.87 1
< 0.1%
4999.85 2
< 0.1%
4999.66 1
< 0.1%
4999.61 1
< 0.1%
4999.54 1
< 0.1%
4999.44 1
< 0.1%
4999.28 2
< 0.1%
4999.27 1
< 0.1%
4999.26 1
< 0.1%
4999.24 1
< 0.1%

Transaction Currency
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
USD
84784 
EUR
21002 
GBP
14100 
AUD
11416 
CAD
9948 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters423750
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSD
2nd rowUSD
3rd rowUSD
4th rowGBP
5th rowUSD

Common Values

ValueCountFrequency (%)
USD 84784
60.0%
EUR 21002
 
14.9%
GBP 14100
 
10.0%
AUD 11416
 
8.1%
CAD 9948
 
7.0%

Length

2025-07-06T16:53:43.727157image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:43.753639image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
usd 84784
60.0%
eur 21002
 
14.9%
gbp 14100
 
10.0%
aud 11416
 
8.1%
cad 9948
 
7.0%

Most occurring characters

ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 423750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 117202
27.7%
D 106148
25.0%
S 84784
20.0%
A 21364
 
5.0%
E 21002
 
5.0%
R 21002
 
5.0%
G 14100
 
3.3%
B 14100
 
3.3%
P 14100
 
3.3%
C 9948
 
2.3%

Processor Response Code
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size5.2 MiB

Processor Response Message
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
Approved
125000 
Refund Processed
 
12500
Chargeback Initiated
 
2500
Chargeback Reversed
 
1250

Length

Max length20
Median length8
Mean length9.0176991
Min length8

Characters and Unicode

Total characters1273750
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowApproved
2nd rowApproved
3rd rowApproved
4th rowApproved
5th rowApproved

Common Values

ValueCountFrequency (%)
Approved 125000
88.5%
Refund Processed 12500
 
8.8%
Chargeback Initiated 2500
 
1.8%
Chargeback Reversed 1250
 
0.9%

Length

2025-07-06T16:53:43.788998image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:43.813159image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
approved 125000
79.4%
refund 12500
 
7.9%
processed 12500
 
7.9%
chargeback 3750
 
2.4%
initiated 2500
 
1.6%
reversed 1250
 
0.8%

Most occurring characters

ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1273750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 250000
19.6%
e 172500
13.5%
d 153750
12.1%
r 142500
11.2%
o 137500
10.8%
v 126250
9.9%
A 125000
9.8%
s 26250
 
2.1%
c 16250
 
1.3%
16250
 
1.3%
Other values (14) 107500
8.4%

Payment Method Type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
Credit Card
47296 
PayPal
47110 
ACH
46844 

Length

Max length11
Median length6
Mean length6.6792779
Min length3

Characters and Unicode

Total characters943448
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowACH
3rd rowACH
4th rowCredit Card
5th rowPayPal

Common Values

ValueCountFrequency (%)
Credit Card 47296
33.5%
PayPal 47110
33.4%
ACH 46844
33.2%

Length

2025-07-06T16:53:43.845622image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:43.868560image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
credit 47296
25.1%
card 47296
25.1%
paypal 47110
25.0%
ach 46844
24.8%

Most occurring characters

ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 943448
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 943448
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 943448
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 141516
15.0%
C 141436
15.0%
r 94592
10.0%
d 94592
10.0%
P 94220
10.0%
e 47296
 
5.0%
i 47296
 
5.0%
t 47296
 
5.0%
47296
 
5.0%
y 47110
 
5.0%
Other values (3) 140798
14.9%

Card Brand
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing93954
Missing (%)66.5%
Memory size7.5 MiB
Mastercard
15960 
Visa
15697 
Amex
15639 

Length

Max length10
Median length4
Mean length6.0246955
Min length4

Characters and Unicode

Total characters284944
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVisa
2nd rowMastercard
3rd rowAmex
4th rowMastercard
5th rowVisa

Common Values

ValueCountFrequency (%)
Mastercard 15960
 
11.3%
Visa 15697
 
11.1%
Amex 15639
 
11.1%
(Missing) 93954
66.5%

Length

2025-07-06T16:53:43.902692image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:43.926978image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
mastercard 15960
33.7%
visa 15697
33.2%
amex 15639
33.1%

Most occurring characters

ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 284944
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 284944
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 284944
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 47617
16.7%
r 31920
11.2%
s 31657
11.1%
e 31599
11.1%
M 15960
 
5.6%
t 15960
 
5.6%
c 15960
 
5.6%
d 15960
 
5.6%
V 15697
 
5.5%
i 15697
 
5.5%
Other values (3) 46917
16.5%

Card Last 4 Digits
Real number (ℝ)

High correlation  Missing 

Distinct8963
Distinct (%)19.0%
Missing93954
Missing (%)66.5%
Infinite0
Infinite (%)0.0%
Mean5511.9391
Minimum1000
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:53:43.961898image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1459.75
Q13267
median5540.5
Q37743.25
95-th percentile9541
Maximum9999
Range8999
Interquartile range (IQR)4476.25

Descriptive statistics

Standard deviation2588.5975
Coefficient of variation (CV)0.46963463
Kurtosis-1.1929835
Mean5511.9391
Median Absolute Deviation (MAD)2238.5
Skewness-0.0087537714
Sum2.6069267 × 108
Variance6700836.9
MonotonicityNot monotonic
2025-07-06T16:53:44.004032image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5062 16
 
< 0.1%
2609 15
 
< 0.1%
9615 15
 
< 0.1%
9126 15
 
< 0.1%
9930 15
 
< 0.1%
7952 14
 
< 0.1%
6197 14
 
< 0.1%
4571 14
 
< 0.1%
3207 14
 
< 0.1%
3872 14
 
< 0.1%
Other values (8953) 47150
33.4%
(Missing) 93954
66.5%
ValueCountFrequency (%)
1000 3
< 0.1%
1001 4
< 0.1%
1002 3
< 0.1%
1003 4
< 0.1%
1004 3
< 0.1%
1005 5
< 0.1%
1006 7
< 0.1%
1007 5
< 0.1%
1008 3
< 0.1%
1009 6
< 0.1%
ValueCountFrequency (%)
9999 7
< 0.1%
9998 8
< 0.1%
9997 7
< 0.1%
9996 5
< 0.1%
9995 5
< 0.1%
9994 6
< 0.1%
9993 5
< 0.1%
9992 7
< 0.1%
9991 8
< 0.1%
9990 7
< 0.1%

Card Expiration Date
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.3 MiB
12/28
141250 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters706250
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12/28
2nd row12/28
3rd row12/28
4th row12/28
5th row12/28

Common Values

ValueCountFrequency (%)
12/28 141250
100.0%

Length

2025-07-06T16:53:44.041273image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:44.059192image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
12/28 141250
100.0%

Most occurring characters

ValueCountFrequency (%)
2 282500
40.0%
1 141250
20.0%
/ 141250
20.0%
8 141250
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 706250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 282500
40.0%
1 141250
20.0%
/ 141250
20.0%
8 141250
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 706250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 282500
40.0%
1 141250
20.0%
/ 141250
20.0%
8 141250
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 706250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 282500
40.0%
1 141250
20.0%
/ 141250
20.0%
8 141250
20.0%

Bank Name
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing94406
Missing (%)66.8%
Memory size7.9 MiB
Bank of Example
46844 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters702660
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBank of Example
2nd rowBank of Example
3rd rowBank of Example
4th rowBank of Example
5th rowBank of Example

Common Values

ValueCountFrequency (%)
Bank of Example 46844
33.2%
(Missing) 94406
66.8%

Length

2025-07-06T16:53:44.080836image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:44.098270image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
bank 46844
33.3%
of 46844
33.3%
example 46844
33.3%

Most occurring characters

ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 702660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 702660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 702660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 93688
13.3%
93688
13.3%
B 46844
 
6.7%
n 46844
 
6.7%
k 46844
 
6.7%
o 46844
 
6.7%
f 46844
 
6.7%
E 46844
 
6.7%
x 46844
 
6.7%
m 46844
 
6.7%
Other values (3) 140532
20.0%

Bank Account Type
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing94406
Missing (%)66.8%
Memory size7.6 MiB
Checking
46844 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters374752
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowChecking
2nd rowChecking
3rd rowChecking
4th rowChecking
5th rowChecking

Common Values

ValueCountFrequency (%)
Checking 46844
33.2%
(Missing) 94406
66.8%

Length

2025-07-06T16:53:44.121399image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:44.139147image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
checking 46844
100.0%

Most occurring characters

ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 374752
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 374752
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 374752
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 46844
12.5%
h 46844
12.5%
e 46844
12.5%
c 46844
12.5%
k 46844
12.5%
i 46844
12.5%
n 46844
12.5%
g 46844
12.5%

Payment Method Token
Text

Missing 

Distinct89597
Distinct (%)94.9%
Missing46844
Missing (%)33.2%
Memory size6.7 MiB
2025-07-06T16:53:44.259801image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters944060
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84954 ?
Unique (%)90.0%

Sample

1st rowTOK_850912
2nd rowTOK_268155
3rd rowTOK_866077
4th rowTOK_244127
5th rowTOK_513827
ValueCountFrequency (%)
tok_962548 4
 
< 0.1%
tok_646979 4
 
< 0.1%
tok_257937 4
 
< 0.1%
tok_974943 4
 
< 0.1%
tok_820511 4
 
< 0.1%
tok_973529 4
 
< 0.1%
tok_769620 4
 
< 0.1%
tok_426828 3
 
< 0.1%
tok_848212 3
 
< 0.1%
tok_265257 3
 
< 0.1%
Other values (89587) 94369
> 99.9%
2025-07-06T16:53:44.425559image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 944060
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 944060
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 944060
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 94406
10.0%
O 94406
10.0%
K 94406
10.0%
_ 94406
10.0%
4 58303
 
6.2%
2 57878
 
6.1%
7 57825
 
6.1%
1 57804
 
6.1%
9 57751
 
6.1%
8 57626
 
6.1%
Other values (4) 219249
23.2%

Merchant Order ID
Text

Unique 

Distinct141250
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.1 MiB
2025-07-06T16:53:44.587411image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters1553750
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique141250 ?
Unique (%)100.0%

Sample

1st rowPAY-0000000
2nd rowPAY-0000001
3rd rowPAY-0000002
4th rowPAY-0000003
5th rowPAY-0000004
ValueCountFrequency (%)
pay-0000000 1
 
< 0.1%
pay-0000011 1
 
< 0.1%
pay-0000017 1
 
< 0.1%
pay-0000016 1
 
< 0.1%
pay-0000015 1
 
< 0.1%
pay-0000014 1
 
< 0.1%
pay-0000013 1
 
< 0.1%
pay-0000012 1
 
< 0.1%
pay-0000010 1
 
< 0.1%
pay-0000070 1
 
< 0.1%
Other values (141240) 141240
> 99.9%
2025-07-06T16:53:44.782611image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1553750
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 334091
21.5%
- 141250
9.1%
P 125000
 
8.0%
Y 125000
 
8.0%
A 125000
 
8.0%
1 107672
 
6.9%
2 73838
 
4.8%
4 68511
 
4.4%
3 68239
 
4.4%
5 67331
 
4.3%
Other values (11) 317818
20.5%
Distinct9000
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size8.5 MiB
2025-07-06T16:53:44.900181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters1977500
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCUST-PROC-2479
2nd rowCUST-PROC-4051
3rd rowCUST-PROC-4566
4th rowCUST-PROC-4425
5th rowCUST-PROC-1238
ValueCountFrequency (%)
cust-proc-1707 32
 
< 0.1%
cust-proc-7936 31
 
< 0.1%
cust-proc-9420 31
 
< 0.1%
cust-proc-6208 30
 
< 0.1%
cust-proc-4665 30
 
< 0.1%
cust-proc-4237 29
 
< 0.1%
cust-proc-7787 29
 
< 0.1%
cust-proc-7756 29
 
< 0.1%
cust-proc-3780 29
 
< 0.1%
cust-proc-6749 29
 
< 0.1%
Other values (8990) 140951
99.8%
2025-07-06T16:53:45.044099image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 282500
14.3%
- 282500
14.3%
U 141250
 
7.1%
S 141250
 
7.1%
T 141250
 
7.1%
P 141250
 
7.1%
R 141250
 
7.1%
O 141250
 
7.1%
4 58176
 
2.9%
7 58165
 
2.9%
Other values (8) 448659
22.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1977500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 282500
14.3%
- 282500
14.3%
U 141250
 
7.1%
S 141250
 
7.1%
T 141250
 
7.1%
P 141250
 
7.1%
R 141250
 
7.1%
O 141250
 
7.1%
4 58176
 
2.9%
7 58165
 
2.9%
Other values (8) 448659
22.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1977500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 282500
14.3%
- 282500
14.3%
U 141250
 
7.1%
S 141250
 
7.1%
T 141250
 
7.1%
P 141250
 
7.1%
R 141250
 
7.1%
O 141250
 
7.1%
4 58176
 
2.9%
7 58165
 
2.9%
Other values (8) 448659
22.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1977500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 282500
14.3%
- 282500
14.3%
U 141250
 
7.1%
S 141250
 
7.1%
T 141250
 
7.1%
P 141250
 
7.1%
R 141250
 
7.1%
O 141250
 
7.1%
4 58176
 
2.9%
7 58165
 
2.9%
Other values (8) 448659
22.7%

AVS Result Code
Boolean

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing93954
Missing (%)66.5%
Memory size276.0 KiB
True
47296 
(Missing)
93954 
ValueCountFrequency (%)
True 47296
33.5%
(Missing) 93954
66.5%
2025-07-06T16:53:45.064890image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

CVV/CVC Result Code
Categorical

Constant  Missing 

Distinct1
Distinct (%)< 0.1%
Missing93954
Missing (%)66.5%
Memory size7.3 MiB
M
47296 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters47296
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 47296
33.5%
(Missing) 93954
66.5%

Length

2025-07-06T16:53:45.084457image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:45.100619image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
m 47296
100.0%

Most occurring characters

ValueCountFrequency (%)
M 47296
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 47296
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 47296
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 47296
100.0%

Fraud Score
Real number (ℝ)

Distinct1001
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.969022
Minimum0
Maximum100
Zeros68
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:53:45.126945image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q125
median49.9
Q375
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)50

Descriptive statistics

Standard deviation28.884486
Coefficient of variation (CV)0.57804785
Kurtosis-1.2018133
Mean49.969022
Median Absolute Deviation (MAD)25
Skewness0.0038236209
Sum7058124.4
Variance834.31353
MonotonicityNot monotonic
2025-07-06T16:53:45.169081image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.3 181
 
0.1%
64.2 178
 
0.1%
31.9 178
 
0.1%
38.6 176
 
0.1%
70.8 175
 
0.1%
0.1 175
 
0.1%
80.8 174
 
0.1%
22.8 173
 
0.1%
89.3 173
 
0.1%
63.9 170
 
0.1%
Other values (991) 139497
98.8%
ValueCountFrequency (%)
0 68
 
< 0.1%
0.1 175
0.1%
0.2 143
0.1%
0.3 152
0.1%
0.4 131
0.1%
0.5 147
0.1%
0.6 131
0.1%
0.7 159
0.1%
0.8 150
0.1%
0.9 143
0.1%
ValueCountFrequency (%)
100 77
0.1%
99.9 126
0.1%
99.8 138
0.1%
99.7 144
0.1%
99.6 156
0.1%
99.5 127
0.1%
99.4 155
0.1%
99.3 126
0.1%
99.2 135
0.1%
99.1 139
0.1%

Fraud Action
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.3 MiB
allow
141250 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters706250
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowallow
2nd rowallow
3rd rowallow
4th rowallow
5th rowallow

Common Values

ValueCountFrequency (%)
allow 141250
100.0%

Length

2025-07-06T16:53:45.206466image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:45.224013image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
allow 141250
100.0%

Most occurring characters

ValueCountFrequency (%)
l 282500
40.0%
a 141250
20.0%
o 141250
20.0%
w 141250
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 706250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 282500
40.0%
a 141250
20.0%
o 141250
20.0%
w 141250
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 706250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 282500
40.0%
a 141250
20.0%
o 141250
20.0%
w 141250
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 706250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 282500
40.0%
a 141250
20.0%
o 141250
20.0%
w 141250
20.0%

Gateway Net Settled Amount
Real number (ℝ)

High correlation 

Distinct124790
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1962.4242
Minimum-5046.2
Maximum4987.37
Zeros0
Zeros (%)0.0%
Negative15000
Negative (%)10.6%
Memory size1.1 MiB
2025-07-06T16:53:45.251315image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-5046.2
5-th percentile-2672.5785
Q1822.68
median2206.12
Q33583.4875
95-th percentile4692.4065
Maximum4987.37
Range10033.57
Interquartile range (IQR)2760.8075

Descriptive statistics

Standard deviation2107.6026
Coefficient of variation (CV)1.0739791
Kurtosis1.0941128
Mean1962.4242
Median Absolute Deviation (MAD)1380.015
Skewness-1.0220654
Sum2.7719242 × 108
Variance4441988.9
MonotonicityNot monotonic
2025-07-06T16:53:45.374734image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3385.57 5
 
< 0.1%
3300.21 5
 
< 0.1%
2897.74 5
 
< 0.1%
4796.47 5
 
< 0.1%
2508.85 4
 
< 0.1%
740.26 4
 
< 0.1%
1054.78 4
 
< 0.1%
1727.27 4
 
< 0.1%
2273.48 4
 
< 0.1%
1768.97 4
 
< 0.1%
Other values (124780) 141206
> 99.9%
ValueCountFrequency (%)
-5046.2 1
< 0.1%
-5045.89 1
< 0.1%
-5045.17 1
< 0.1%
-5044.44 1
< 0.1%
-5044.03 1
< 0.1%
-5042.96 1
< 0.1%
-5042.84 1
< 0.1%
-5042.64 1
< 0.1%
-5039.5 1
< 0.1%
-5039.08 1
< 0.1%
ValueCountFrequency (%)
4987.37 1
< 0.1%
4987.35 1
< 0.1%
4987.11 1
< 0.1%
4987.04 1
< 0.1%
4986.94 1
< 0.1%
4986.78 2
< 0.1%
4986.68 1
< 0.1%
4986.35 1
< 0.1%
4986.34 1
< 0.1%
4986.23 1
< 0.1%

Processor Fees
Real number (ℝ)

High correlation  Zeros 

Distinct7087
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8112149
Minimum0
Maximum74.99
Zeros93954
Zeros (%)66.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2025-07-06T16:53:45.415329image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314.46
95-th percentile47.98
Maximum74.99
Range74.99
Interquartile range (IQR)14.46

Descriptive statistics

Standard deviation17.32999
Coefficient of variation (CV)1.7663449
Kurtosis1.9714233
Mean9.8112149
Median Absolute Deviation (MAD)0
Skewness1.7263428
Sum1385834.1
Variance300.32854
MonotonicityNot monotonic
2025-07-06T16:53:45.457312image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 93954
66.5%
41.01 24
 
< 0.1%
38.4 24
 
< 0.1%
15.22 24
 
< 0.1%
9.17 23
 
< 0.1%
12.85 23
 
< 0.1%
9.3 23
 
< 0.1%
17.81 23
 
< 0.1%
27.07 23
 
< 0.1%
28.66 22
 
< 0.1%
Other values (7077) 47087
33.3%
ValueCountFrequency (%)
0 93954
66.5%
0.26 5
 
< 0.1%
0.27 9
 
< 0.1%
0.28 7
 
< 0.1%
0.29 3
 
< 0.1%
0.3 10
 
< 0.1%
0.31 2
 
< 0.1%
0.32 7
 
< 0.1%
0.33 7
 
< 0.1%
0.34 3
 
< 0.1%
ValueCountFrequency (%)
74.99 1
 
< 0.1%
74.98 3
< 0.1%
74.97 2
 
< 0.1%
74.95 2
 
< 0.1%
74.94 5
< 0.1%
74.93 4
< 0.1%
74.92 1
 
< 0.1%
74.91 1
 
< 0.1%
74.9 2
 
< 0.1%
74.89 2
 
< 0.1%

Net Amount to Merchant
Real number (ℝ)

High correlation 

Distinct126072
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1952.613
Minimum-5121.14
Maximum4987.37
Zeros0
Zeros (%)0.0%
Negative15000
Negative (%)10.6%
Memory size1.1 MiB
2025-07-06T16:53:45.497774image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-5121.14
5-th percentile-2686.679
Q1818.9725
median2198.58
Q33568.6375
95-th percentile4673.3085
Maximum4987.37
Range10108.51
Interquartile range (IQR)2749.665

Descriptive statistics

Standard deviation2104.7798
Coefficient of variation (CV)1.0779298
Kurtosis1.1223452
Mean1952.613
Median Absolute Deviation (MAD)1375.175
Skewness-1.0310854
Sum2.7580658 × 108
Variance4430098
MonotonicityNot monotonic
2025-07-06T16:53:45.538456image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
398.52 5
 
< 0.1%
3598.04 5
 
< 0.1%
453.83 5
 
< 0.1%
4242.54 4
 
< 0.1%
3644.56 4
 
< 0.1%
4329.09 4
 
< 0.1%
1088.79 4
 
< 0.1%
1003.37 4
 
< 0.1%
2042.33 4
 
< 0.1%
257.12 4
 
< 0.1%
Other values (126062) 141207
> 99.9%
ValueCountFrequency (%)
-5121.14 1
< 0.1%
-5120.1 1
< 0.1%
-5114.34 1
< 0.1%
-5112.98 1
< 0.1%
-5112.86 2
< 0.1%
-5107.75 1
< 0.1%
-5097.22 1
< 0.1%
-5095.85 1
< 0.1%
-5094.91 1
< 0.1%
-5094.38 1
< 0.1%
ValueCountFrequency (%)
4987.37 1
< 0.1%
4987.35 1
< 0.1%
4987.11 1
< 0.1%
4987.04 1
< 0.1%
4986.94 1
< 0.1%
4986.78 2
< 0.1%
4986.68 1
< 0.1%
4986.35 1
< 0.1%
4986.34 1
< 0.1%
4986.23 1
< 0.1%
Distinct2250
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size10.6 MiB
2025-07-06T16:53:45.643784image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length38
Median length32
Mean length30.014386
Min length26

Characters and Unicode

Total characters4239532
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSET-20250130-PayPal-N/A-USD
2nd rowSET-20250110-GoCardless-N/A-USD
3rd rowSET-20250221-GoCardless-N/A-USD
4th rowSET-20250104-ClientLine-Visa-GBP
5th rowSET-20250316-PayPal-N/A-USD
ValueCountFrequency (%)
set-20250102-paypal-n/a-usd 360
 
0.3%
set-20250202-paypal-n/a-usd 359
 
0.3%
set-20250120-paypal-n/a-usd 352
 
0.2%
set-20250314-paypal-n/a-usd 350
 
0.2%
set-20250313-gocardless-n/a-usd 349
 
0.2%
set-20250112-paypal-n/a-usd 348
 
0.2%
set-20250115-gocardless-n/a-usd 346
 
0.2%
set-20250304-paypal-n/a-usd 345
 
0.2%
set-20250316-paypal-n/a-usd 344
 
0.2%
set-20250202-gocardless-n/a-usd 344
 
0.2%
Other values (2240) 137753
97.5%
2025-07-06T16:53:45.787796image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 565000
 
13.3%
2 386343
 
9.1%
0 337359
 
8.0%
S 226034
 
5.3%
a 188681
 
4.5%
E 162252
 
3.8%
e 157396
 
3.7%
5 155521
 
3.7%
A 146596
 
3.5%
T 141250
 
3.3%
Other values (31) 1773100
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4239532
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 565000
 
13.3%
2 386343
 
9.1%
0 337359
 
8.0%
S 226034
 
5.3%
a 188681
 
4.5%
E 162252
 
3.8%
e 157396
 
3.7%
5 155521
 
3.7%
A 146596
 
3.5%
T 141250
 
3.3%
Other values (31) 1773100
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4239532
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 565000
 
13.3%
2 386343
 
9.1%
0 337359
 
8.0%
S 226034
 
5.3%
a 188681
 
4.5%
E 162252
 
3.8%
e 157396
 
3.7%
5 155521
 
3.7%
A 146596
 
3.5%
T 141250
 
3.3%
Other values (31) 1773100
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4239532
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 565000
 
13.3%
2 386343
 
9.1%
0 337359
 
8.0%
S 226034
 
5.3%
a 188681
 
4.5%
E 162252
 
3.8%
e 157396
 
3.7%
5 155521
 
3.7%
A 146596
 
3.5%
T 141250
 
3.3%
Other values (31) 1773100
41.8%
Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-01-02 00:00:00
Maximum2025-04-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:53:45.851584image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:45.926548image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct47296
Distinct (%)100.0%
Missing93954
Missing (%)66.5%
Memory size5.6 MiB
2025-07-06T16:53:46.040678image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters567552
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47296 ?
Unique (%)100.0%

Sample

1st rowARN618188908
2nd rowARN591055160
3rd rowARN184492302
4th rowARN314680266
5th rowARN615595718
ValueCountFrequency (%)
arn276300877 1
 
< 0.1%
arn213177541 1
 
< 0.1%
arn669849388 1
 
< 0.1%
arn629677896 1
 
< 0.1%
arn591055160 1
 
< 0.1%
arn184492302 1
 
< 0.1%
arn314680266 1
 
< 0.1%
arn615595718 1
 
< 0.1%
arn594832966 1
 
< 0.1%
arn508354528 1
 
< 0.1%
Other values (47286) 47286
> 99.9%
2025-07-06T16:53:46.175404image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 567552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 567552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 567552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 47296
 
8.3%
R 47296
 
8.3%
N 47296
 
8.3%
2 43333
 
7.6%
3 43163
 
7.6%
6 43124
 
7.6%
7 43103
 
7.6%
9 43094
 
7.6%
5 43048
 
7.6%
8 43038
 
7.6%
Other values (3) 123761
21.8%
Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-01-01 00:00:00
Maximum2025-03-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:53:46.233837image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:46.282381image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-01-01 00:00:00
Maximum2025-03-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:53:46.329721image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:46.374923image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct90
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-01-01 00:00:00
Maximum2025-03-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:53:46.419997image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:46.467129image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct65
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Minimum2025-01-02 00:00:00
Maximum2025-04-02 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-06T16:53:46.511094image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:46.558723image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Payment Gateway Type
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
Cybersource
47296 
PayPal
47110 
GoCardless
46844 

Length

Max length11
Median length10
Mean length9.0007504
Min length6

Characters and Unicode

Total characters1271356
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPayPal
2nd rowGoCardless
3rd rowGoCardless
4th rowCybersource
5th rowPayPal

Common Values

ValueCountFrequency (%)
Cybersource 47296
33.5%
PayPal 47110
33.4%
GoCardless 46844
33.2%

Length

2025-07-06T16:53:46.598123image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-06T16:53:46.619610image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
cybersource 47296
33.5%
paypal 47110
33.4%
gocardless 46844
33.2%

Most occurring characters

ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1271356
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 141436
11.1%
r 141436
11.1%
a 141064
11.1%
s 140984
11.1%
y 94406
7.4%
P 94220
7.4%
C 94140
7.4%
o 94140
7.4%
l 93954
7.4%
b 47296
 
3.7%
Other values (4) 188280
14.8%

Interactions

2025-07-06T16:53:41.969612image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:40.901921image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.115332image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.320395image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.538720image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.747973image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.999942image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:40.937035image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.149613image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.352577image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.570935image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.783019image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:42.034733image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:40.974717image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.179321image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.389290image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.605926image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.820246image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:42.072223image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.010438image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.215919image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.424192image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.642661image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.857316image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:42.108234image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.045610image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.250255image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.462302image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.675913image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.895489image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:42.145231image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.081738image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.285760image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.503111image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.714346image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-07-06T16:53:41.931955image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-07-06T16:53:46.650181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Card BrandCard Last 4 DigitsFraud ScoreGateway Net Settled AmountMerchant Account IDMerchant Processor TypeNet Amount to MerchantPayment Gateway TypePayment Method TypeProcessor FeesProcessor Response MessageTransaction AmountTransaction CurrencyTransaction StatusTransaction Type
Card Brand1.0000.0000.0000.0120.0061.0000.0131.0001.0000.3360.0000.0120.0060.0000.000
Card Last 4 Digits0.0001.000-0.0060.0050.0000.0000.0051.0001.0000.0040.0000.0050.0000.0000.000
Fraud Score0.000-0.0061.0000.0010.0030.0000.0010.0010.0010.0020.0030.0010.0050.0080.003
Gateway Net Settled Amount0.0120.0050.0011.0000.0020.0041.0000.0000.0000.0950.5771.0000.0040.3000.577
Merchant Account ID0.0060.0000.0030.0021.0000.8160.0051.0001.0000.3020.0000.0001.0000.0060.000
Merchant Processor Type1.0000.0000.0000.0040.8161.0000.0111.0001.0000.5890.0000.0000.0010.0040.000
Net Amount to Merchant0.0130.0050.0011.0000.0050.0111.0000.0120.0120.0860.5751.0000.0040.3000.575
Payment Gateway Type1.0001.0000.0010.0001.0001.0000.0121.0001.0000.6400.0000.0000.0000.0050.000
Payment Method Type1.0001.0000.0010.0001.0001.0000.0121.0001.0000.6400.0000.0000.0000.0050.000
Processor Fees0.3360.0040.0020.0950.3020.5890.0860.6400.6401.0000.0060.0980.0010.0100.006
Processor Response Message0.0000.0000.0030.5770.0000.0000.5750.0000.0000.0061.0000.5770.0001.0001.000
Transaction Amount0.0120.0050.0011.0000.0000.0001.0000.0000.0000.0980.5771.0000.0040.3010.577
Transaction Currency0.0060.0000.0050.0041.0000.0010.0040.0000.0000.0010.0000.0041.0000.0000.000
Transaction Status0.0000.0000.0080.3000.0060.0040.3000.0050.0050.0101.0000.3010.0001.0001.000
Transaction Type0.0000.0000.0030.5770.0000.0000.5750.0000.0000.0061.0000.5770.0001.0001.000

Missing values

2025-07-06T16:53:42.334117image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-06T16:53:42.607927image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-06T16:53:42.961058image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Processor Transaction IDMerchant Account IDMerchant Processor TypeTransaction TypeTransaction StatusTransaction AmountTransaction CurrencyProcessor Response CodeProcessor Response MessagePayment Method TypeCard BrandCard Last 4 DigitsCard Expiration DateBank NameBank Account TypePayment Method TokenMerchant Order IDCustomer IDAVS Result CodeCVV/CVC Result CodeFraud ScoreFraud ActionGateway Net Settled AmountProcessor FeesNet Amount to MerchantSettlement Batch IDSettlement DateAcquirer Reference Number (ARN)Transaction TimestampLast Update TimestampPayment Gateway DatePayout DatePayment Gateway Type
0PROC-TRX-96148747MERCH_PAYPAL_USDPayPalpaymentsettled2592.98USD0ApprovedPayPalNaNNaN12/28NaNNaNTOK_850912PAY-0000000CUST-PROC-2479NaNNaN60.6allow2573.530.002573.53SET-20250130-PayPal-N/A-USD2025-01-31NaN2025-01-302025-01-302025-01-302025-01-31PayPal
1PROC-TRX-53756634MERCH_GOCARDLESS_USDGoCardlesspaymentsettled4277.28USD0ApprovedACHNaNNaN12/28Bank of ExampleCheckingNaNPAY-0000001CUST-PROC-4051NaNNaN0.6allow4266.590.004266.59SET-20250110-GoCardless-N/A-USD2025-01-13NaN2025-01-102025-01-102025-01-102025-01-13GoCardless
2PROC-TRX-96643401MERCH_GOCARDLESS_USDGoCardlesspaymentsettled2515.78USD0ApprovedACHNaNNaN12/28Bank of ExampleCheckingNaNPAY-0000002CUST-PROC-4566NaNNaN2.4allow2509.490.002509.49SET-20250221-GoCardless-N/A-USD2025-02-24NaN2025-02-212025-02-212025-02-212025-02-24GoCardless
3PROC-TRX-48503361MERCH_CYBERSOURCE_GBPClientLinepaymentsettled2874.81GBP0ApprovedCredit CardVisa9887.012/28NaNNaNTOK_268155PAY-0000003CUST-PROC-4425YM12.6allow2846.0628.752817.31SET-20250104-ClientLine-Visa-GBP2025-01-07ARN6181889082025-01-042025-01-042025-01-042025-01-07Cybersource
4PROC-TRX-87613870MERCH_PAYPAL_USDPayPalpaymentsettled350.01USD0ApprovedPayPalNaNNaN12/28NaNNaNTOK_866077PAY-0000004CUST-PROC-1238NaNNaN63.3allow347.380.00347.38SET-20250316-PayPal-N/A-USD2025-03-19NaN2025-03-162025-03-162025-03-162025-03-19PayPal
5PROC-TRX-40189275MERCH_CYBERSOURCE_USDClientLinepaymentsettled4412.91USD0ApprovedCredit CardMastercard9534.012/28NaNNaNTOK_244127PAY-0000005CUST-PROC-9562YM93.4allow4368.7844.134324.65SET-20250210-ClientLine-Mastercard-USD2025-02-11ARN5910551602025-02-102025-02-102025-02-102025-02-11Cybersource
6PROC-TRX-13724055MERCH_GOCARDLESS_AUDGoCardlesspaymentsettled399.15AUD0ApprovedACHNaNNaN12/28Bank of ExampleCheckingNaNPAY-0000006CUST-PROC-8369NaNNaN96.5allow398.150.00398.15SET-20250207-GoCardless-N/A-AUD2025-02-10NaN2025-02-072025-02-072025-02-072025-02-10GoCardless
7PROC-TRX-57320369MERCH_GOCARDLESS_GBPGoCardlesspaymentsettled1639.12GBP0ApprovedACHNaNNaN12/28Bank of ExampleCheckingNaNPAY-0000007CUST-PROC-5373NaNNaN50.8allow1635.020.001635.02SET-20250220-GoCardless-N/A-GBP2025-02-21NaN2025-02-202025-02-202025-02-202025-02-21GoCardless
8PROC-TRX-36380772MERCH_CYBERSOURCE_USDAmexpaymentsettled802.45USD0ApprovedCredit CardAmex2535.012/28NaNNaNTOK_513827PAY-0000008CUST-PROC-6283YM41.0allow794.4312.04782.39SET-20250225-Amex-Amex-USD2025-02-26ARN1844923022025-02-252025-02-252025-02-252025-02-26Cybersource
9PROC-TRX-55314596MERCH_CYBERSOURCE_USDClientLinepaymentsettled4780.71USD0ApprovedCredit CardMastercard6108.012/28NaNNaNTOK_505369PAY-0000009CUST-PROC-2142YM33.8allow4732.9047.814685.09SET-20250223-ClientLine-Mastercard-USD2025-02-26ARN3146802662025-02-232025-02-232025-02-232025-02-26Cybersource
Processor Transaction IDMerchant Account IDMerchant Processor TypeTransaction TypeTransaction StatusTransaction AmountTransaction CurrencyProcessor Response CodeProcessor Response MessagePayment Method TypeCard BrandCard Last 4 DigitsCard Expiration DateBank NameBank Account TypePayment Method TokenMerchant Order IDCustomer IDAVS Result CodeCVV/CVC Result CodeFraud ScoreFraud ActionGateway Net Settled AmountProcessor FeesNet Amount to MerchantSettlement Batch IDSettlement DateAcquirer Reference Number (ARN)Transaction TimestampLast Update TimestampPayment Gateway DatePayout DatePayment Gateway Type
141240PROC-TRX-32788723MERCH_GOCARDLESS_USDGoCardlesschargeback_reversaldisputed689.24USDC02Chargeback ReversedACHNaNNaN12/28Bank of ExampleCheckingNaNREV-0107910CUST-PROC-6672NaNNaN69.5allow687.520.00687.52SET-20250326-GoCardless-N/A-USD2025-03-27NaN2025-03-262025-03-262025-03-262025-03-27GoCardless
141241PROC-TRX-72456201MERCH_CYBERSOURCE_USDClientLinechargeback_reversaldisputed2318.44USDC02Chargeback ReversedCredit CardMastercard5928.012/28NaNNaNTOK_629291REV-0024103CUST-PROC-9714YM17.3allow2295.2623.182272.08SET-20250306-ClientLine-Mastercard-USD2025-03-07ARN2631112602025-03-062025-03-062025-03-062025-03-07Cybersource
141242PROC-TRX-53974940MERCH_CYBERSOURCE_CADAmexchargeback_reversaldisputed4353.13CADC02Chargeback ReversedCredit CardAmex8590.012/28NaNNaNTOK_952318REV-0039969CUST-PROC-6116YM33.3allow4309.6065.304244.30SET-20250311-Amex-Amex-CAD2025-03-12ARN1354149272025-03-112025-03-112025-03-112025-03-12Cybersource
141243PROC-TRX-11820156MERCH_PAYPAL_GBPPayPalchargeback_reversaldisputed28.61GBPC02Chargeback ReversedPayPalNaNNaN12/28NaNNaNTOK_718010REV-0101060CUST-PROC-1705NaNNaN43.7allow28.400.0028.40SET-20250131-PayPal-N/A-GBP2025-02-03NaN2025-01-312025-01-312025-01-312025-02-03PayPal
141244PROC-TRX-37925899MERCH_GOCARDLESS_USDGoCardlesschargeback_reversaldisputed2263.30USDC02Chargeback ReversedACHNaNNaN12/28Bank of ExampleCheckingNaNREV-0020178CUST-PROC-5907NaNNaN43.4allow2257.640.002257.64SET-20250124-GoCardless-N/A-USD2025-01-27NaN2025-01-242025-01-242025-01-242025-01-27GoCardless
141245PROC-TRX-26208812MERCH_GOCARDLESS_USDGoCardlesschargeback_reversaldisputed2573.44USDC02Chargeback ReversedACHNaNNaN12/28Bank of ExampleCheckingNaNREV-0025245CUST-PROC-6978NaNNaN69.0allow2567.010.002567.01SET-20250312-GoCardless-N/A-USD2025-03-13NaN2025-03-122025-03-122025-03-122025-03-13GoCardless
141246PROC-TRX-55820069MERCH_CYBERSOURCE_EURAmexchargeback_reversaldisputed794.03EURC02Chargeback ReversedCredit CardAmex8120.012/28NaNNaNTOK_778198REV-0093761CUST-PROC-4520YM36.2allow786.0911.91774.18SET-20250101-Amex-Amex-EUR2025-01-02ARN1311111692025-01-012025-01-012025-01-012025-01-02Cybersource
141247PROC-TRX-14765444MERCH_PAYPAL_USDPayPalchargeback_reversaldisputed3629.53USDC02Chargeback ReversedPayPalNaNNaN12/28NaNNaNTOK_797724REV-0103102CUST-PROC-9579NaNNaN85.2allow3602.310.003602.31SET-20250213-PayPal-N/A-USD2025-02-14NaN2025-02-132025-02-132025-02-132025-02-14PayPal
141248PROC-TRX-18452065MERCH_CYBERSOURCE_USDClientLinechargeback_reversaldisputed2700.28USDC02Chargeback ReversedCredit CardMastercard8696.012/28NaNNaNTOK_890568REV-0039089CUST-PROC-2021YM5.8allow2673.2827.002646.28SET-20250210-ClientLine-Mastercard-USD2025-02-11ARN8946575582025-02-102025-02-102025-02-102025-02-11Cybersource
141249PROC-TRX-40563623MERCH_GOCARDLESS_CADGoCardlesschargeback_reversaldisputed2500.12CADC02Chargeback ReversedACHNaNNaN12/28Bank of ExampleCheckingNaNREV-0022422CUST-PROC-1156NaNNaN76.2allow2493.870.002493.87SET-20250105-GoCardless-N/A-CAD2025-01-08NaN2025-01-052025-01-052025-01-052025-01-08GoCardless